A regional integrated assessment of the impacts of climate change and of the potential adaptation avenues for Quebec's forests
Bibliographic record
Abstract
Regional analyses assessing the vulnerabilities of forest ecosystems and the forest sector to climate change are key to considering the heterogeneity of climate change impacts as well as the fact that risks, opportunities, and adaptation capacities might differ regionally. Here we provide the Regional Integrated Assessment of climate change on Quebec's forests, a work that involved several research teams and focused on climate change impacts on Quebec's commercial forests and on potential adaptation solutions. Our work showed that climate change will alter several ecological processes within Quebec's forests. These changes will result in important modifications in forest landscapes. Harvest will cumulate with climate change effects to further alter future forest landscapes, which will also have consequences on wildlife habitats (including woodland caribou habitat), avian biodiversity, carbon budget, and a variety of forest landscape values for Indigenous peoples. The adaptation of the forest sector will be crucial to mitigate the impacts of climate change on forest ecosystem goods and services and improve their resilience. Moving forward, a broad range of adaptation measures, notably through reducing harvest levels, should be explored to help strike a balance among social, ecological, and economic values. We conclude that without climate adaptation, strong negative economic and ecological impacts will likely affect Quebec's forests.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".